Stock price trend prediction with long short-term memory neural networks

2019 ◽  
Vol 8 (4) ◽  
pp. 289
Author(s):  
Varun Gupta ◽  
Mujahid Ahmad
Keyword(s):  
Neural Networks ◽  
Stock Price ◽  
Short Term Memory ◽  
Short Term ◽  
Trend Prediction ◽  
Price Trend ◽  
Term Memory ◽  
Long Short Term Memory

An Efficient Hybrid Mechanism with LSTM Neural Networks in Application to Stock Price Forecasting

2020 ◽  
Author(s):  
Ngoc-An Nguyen-Pham ◽  
Trung T. Nguyen
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Stock Price ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Practical Applications ◽  
Hybrid Mechanism ◽  
Dual Stage ◽  
Long Short Term Memory

Recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks have shown some success with many practical applications in recent years such as machine translation, speech recognition, image processing and financial market forecasting. In recent years, a dual-stage attention-based recurrent neural network (DA-RNN) has shown some promising results on stock price prediction. We propose dual attention-dilated long short-term memory (DAD-LSTM) models combining DA-RNN and dilated recurrent neural networks (DRNN) to select the most relevant input features and capture the long-term temporal dependencies of a time series more efficiently. Numerical results from experiments on the NASDAQ 100, S&P 500, HSI and DJIA datasets show that DAD-LSTM models outperform the state-of-the-art and most recent approaches.

scholarly journals Stock Price Trend Forecasting using Long Short Term Memory Recurrent Neural Networks

2020 ◽  
pp. 468-474
Author(s):  
Mahdi Ismael Omar ◽  
Mujeeb Rahaman
Keyword(s):  
Stock Price ◽  
Short Term Memory ◽  
Large Data ◽  
Research Problem ◽  
Classification Problem ◽  
Short Term ◽  
Trading System ◽  
Price Trend ◽  
Term Memory ◽  
Long Short Term Memory

The prediction of future stock price trend using current and historical stock market data is a research problem for traders and researchers. Recently deep learning methods shown promising performance to extract meaningful information from the given large data. In this paper, we proposed a system to predict the next trading session close price trend from historical stock trading data using long short term memory (LSTM) method. This is a classification problem next trading session close price trend can be uptrend, downtrend, or sideways trend. We built an automated trading system using the results of our classifier. We experimented with the proposed trading system on the American index stocks. Our experimental results show that the proposed method outperforms the buy-and-hold and decision tree-based method.

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